Systematic Review of Identity-Centric Security in Cloud-Native CI/CD Pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As cloud-native CI/CD pipelines automate software delivery at scale, identity-centric security has become a critical concern. This paper reports a systematic literature review of 59 peer-reviewed studies that examine authentication and authorisation (AuthN/AuthZ) in CI/CD workflows. We synthesise key vulnerability classes, including token theft, privilege escalation, session hijacking, supply-chain abuse, and misaligned microservice identities. We then introduce a CI/CD-specific vulnerability taxonomy and systematically map established mechanisms such as OAuth 2.0, Kerberos, SAML, mTLS, RBAC/ABAC, XACML, API gateways, and MFA to the attack vectors they mitigate across the pipeline. Finally, we analyse emerging trends, including Zero-Trust Architecture, decentralised identity, service-mesh-based access control, and cryptographically anchored identity models that use blockchain and self-sovereign identity. The review exposes persistent gaps in configuration, observability, and runtime enforcement, as well as organisational barriers to adopting stronger identity controls. Our findings provide a structured foundation for designing trustworthy, identity-centric DevSecOps practices and highlight concrete research directions for securing access in cloud-native CI/CD environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it